hn-classics/_stories/1985/14443638.md

---
created_at: '2017-05-30T08:06:21.000Z'
title: On anthropomorphism in science (1985)
url: https://www.cs.utexas.edu/users/EWD/transcriptions/EWD09xx/EWD936.html
author: dopkew
points: 70
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comment_text: 
num_comments: 62
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created_at_i: 1496131581
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objectID: '14443638'
year: 1985

---
On anthropomorphism in science

(Delivered at The Philosophers’ Lunch, 25 September 1985)

I must apologize for not speaking to you, but reading to you. I chose to
do so because, not yet feeling quite at home, I am a bit nervous. Of
course I can argue to myself that I don’t need to, but that does not
always work.

I can argue to myself that I grew up in a country whose population is
only slightly larger than that of Texas, so why should I feel not at
home? I spent most of my life at two universities, one four centuries
old, the other a quarter, and if I take the geometric mean of those two
ages I arrive precisely at that of UT, so why shouldn’t I feel at home
here?

Well, actually it is not too bad. I think I am much happier here than I
would have been, say, at XXX–XXX where it is possible to lose sight of
what it means to be an intellectual. The reason that I am a bit nervous
is that I am not quite sure what philosophers do and, hence, somewhat
uncertain about my role here.

OK, so much for an irrelevant introduction; it was given to give you the
opportunity to adapt your ear to my English.

\* \* \*

I chose “anthropomorphism” because —besides being a nice broad topic— it
is so pervasive that many of my colleagues don’t realize how pernicious
it is.

Let me first relate my experience that drove home how pervasive
anthropomorphism is. It took place at one of the monthly meetings of the
science section of the Royal Netherlands Academy of Arts and Sciences,
where we were shown a motion picture made through a microscope. Thanks
to phase contrast microscopy —the invention for which Zernike got the
Nobel Prize— it is now possible to see through the microscope undyed
cultures of living cells, and that was what they had done while making
this motion picture. It showed us —somewhat accelerated— the life of a
culture of amoebae. For quite a while we looked at something we had
never seen: I can only describe it as identifiable bubbles with
irregular changing contours, slowly moving without any pattern through a
two-dimensional aquarium. To all intents and purposes it could have been
some sort of dynamic wallpaper. It was, in fact, rather boring, looking
at those aimlessly moving grey blots, until one of the amoeba in the
centre of the screen began to divide. We saw it constrict, we saw in
succession all the images familiar from our high-school biology, we saw
the centres of the two halves move in opposite directions until they
were only connected by a thin thread as they began to pull more
frantically at either end of the leash that still connected them.
Finally the connection broke and the two swam away from each other at
the maximum speed young amoebae can muster.

The fascinating and somewhat frightening observation, however, was that
at the moment of the rupture one hundred otherwise respectable
scientists gave all a sigh of relief: “at last they had succeeded in
freeing themselves from each other.” None of us had been able to resist,
as the division process went on, the temptation to discern two
individuals with which we could identify and of which we felt —more in
our bones than in our brains, but that is beside the point— how much
they “wanted” to get loose. A whole pattern of human desires had been
projected on those blots\! Crazy, of course, but such is the pervasive
and insidious habit of anthropomorphic thought.

Is anthropomorphic thinking bad? Well, it is certainly no good in the
sense that it does not help. Why did the stone fall in Greek antiquity?
Quite simply because it wanted to go to the centre of the earth. And,
several centuries later, we had the burning question: why do stones want
to go to the centre of the earth? Well, that is simple too: because
that’s where they belong. Why are heavier stones heavier than lighter
stones? Because they are more eager to be at the centre of the earth.
But then Galileo made the troubling discovery that the heavier stone
does not fall any faster than the lighter one. How come? Simple, dear
Watson: the heavier stone has indeed a greater desire to be at the
centre of the earth, but it is also more lazy. So much for a —be it
somewhat simplified— history of the development of physics. I trust you
got the message.

So anthropomorphic thinking is no good in the sense that it does not
help. But is it also bad? Yes, it is, because even if we can point to
some analogy between Man and Thing, the analogy is always negligible in
comparison to the differences, and as soon as we allow ourselves to be
seduced by the analogy to describe the Thing in anthropomorphic
terminology, we immediately lose our control over which human
connotations we drag into the picture. And as most of those are totally
inadequate, the anthropomorphism becomes more misleading than helpful.

I started as a theoretical physicist, became involved in computing and
may end up as a mathematician. It is specifically my connection with
computing that has made me allergic, since computing science is cursed
by a rampant anthropomorphism.

This has been so right from its inception, and found its way in the
public perception of the topic, as is illustrated by the title of the
book that Edmund C. Berkeley published in the fifties: “Giant Brains or
Machines that Think”. The simplest way of showing how preposterous that
title is is by pointing at its two companion volumes —still to be
written— “Giant Hearts or Machines that Fall in Love” and “Giant Souls
or Machines that Believe in God”, the most fascinating feature of the
latter, of course, being that they can believe in God much faster than
you. Regrettably we cannot sweep this nonsense under the rug by saying
“Why bother? This is only popular press”. It finds its echo in
publications that are intended to be serious, such as Grace M. Hopper’s
article with the title “The education of a computer.”. It also finds its
reflection in the multi-billion yen mistake of the Japanese “fifth
generation computer project”, of which you may have heard. It would have
taken care of the Japanese competition; regrettably —for the Western
world— they seem to come to their senses, as the larger Japanese
companies are pulling out of the efforts aimed at blurring the
distinction between Man and Machine.

But the blur continues to linger on, and has a much wider impact than
you might suspect. You see, it is not only that the question “Can
machines think?” is regularly raised; we can —and should— deal with that
by pointing out that it is just as relevant as the equally burning
question “Can submarines swim?” A more serious byproduct of the tendency
to talk about machines in anthropomorphic terms is the companion
phenomenon of talking about people in mechanistic terminology. The
critical reading of articles about computer-assisted learning —excuse
me: CAL for the intimi— leaves you no option: in the eyes of their
authors, the educational process is simply reduced to a caricature,
something like the building up of conditional reflexes. For those
educationists, Pavlov’s dog adequately captures the essence of Mankind
—while I can assure you, from intimate observations, that it only
captures a minute fraction of what is involved in being a dog—.

The anthropomorphic metaphor is perhaps even more devastating within
computing science itself. Its use is almost all-pervading. To give you
just an example: entering a lecture hall at a conference I caught just
one sentence and quickly went out again. The sentence started with “When
this guy wants to talk to that guy...”. The speaker referred to two
components of a computer network.

The trouble with the metaphor is, firstly, that it invites you to
identify yourself with the computational processes going on in system
components and, secondly, that we see ourselves as existing in time.
Consequently the use of the metaphor forces one to what we call
“operational reasoning”, that is reasoning in terms of the
computational processes that could take place. From a methodological
point of view this is a well-identified and well-documented mistake: it
induces a combinatorial explosion of the number of cases to consider and
designs thus conceived are as a result full of bugs.

It is possible to base one’s reasoning on non-operational semantics and
to design for instance one’s programs by manipulating one’s program text
as a formal object in its own right, in one’s arguments completely
ignoring that these texts also admit the interpretation of executable
code. By ignoring the computational processes one saves oneself from the
combinatorial explosion. This nonoperational approach is the only known
reliable way of digital system design, and enables you to publish for
instance in full confidence intricate algorithms you designed but never
tested on a machine. The implied abstraction, in which time has
disappeared from the picture, is however beyond the computing scientist
imbued with the operational approach that the anthropomorphic metaphor
induces. In a very real and tragic sense he has a mental block: his
anthropomorphic thinking erects an insurmountable barrier between him
and the only effective way in which his work can be done well. By the
prevailing anthropomorphism the US, computer industry could easily be
done in.

It is not only the industry that suffers, so does the science. Recently,
a whole group of computing scientists from all over the world has wasted
several years of effort. They had decided to apply to the relationship
between a component and its environment a dichotomy: the “obligations”
of the environment versus the “responsibilities” of the component. The
terminology alone should have been sufficient to make them very
suspicious; it did not and they learned the hard way that the whole
distinction did not make sense.

Another notion that creeps in as a result of our anthropomorphism is the
dichotomy of cause and effect. These terms come from our perception of
our intended acts: we wish to pour ourselves a glass of wine, so we pick
up the bottle and turn it, thereby causing the wine to flow from the
bottle into our glass. Our act of pouring had the desired effect. But in
the inanimate world there is little place for such a causal hierarchy.
One of Newton’s Laws says that force equals mass times acceleration, and
there is no point in insisting that the one causes the other or the
other way round: they are equal. In the case of a piezo-electric crystal
deformation and voltage difference are accompanying phenomena: if one
applies a voltage difference, the crystal changes its shape, if the
crystal is deformed, a voltage difference appears (as we all know from
the butane cigarette lighter).

In particular the study of distributed computer systems has severely
suffered from the vain effort to impose a causal hierarchy on the events
that constitute a computational process, thus completely hiding the
symmetry between the sending and the receiving of messages, and between
input and output.

But even in the so much more abstract world of mathematics this has
created havoc. It has caused a preponderance of mathematical structures
of the form: “If A then B” or equivalently “A implies B”. Take good old
Pythagoras

> “If, in triangle ABC, angle C is right, then a2+b2=c2”.

but we have equally well

> “If, in triangle ABC, a2+b2=c2, then angle C is right”.

and the proper way of stating Pythagoras’s Theorem is by saying that in
triangle ABC “a2+b2=c2” and “angle C is right” are equivalent
propositions, either both true or both false. Analyzing the structure of
traditional mathematical arguments one will discover that the
equivalence is the most underexploited logical connective, in contrast
to the implication that is used all over the place. The
underexploitation of the equivalence, i.e. the failure to exploit
inherent symmetries, often lengthens an argument by a factor of 2, 4 or
more.

Why then have mathematicians stuck to the implication? Well, because
they feel comfortable with it because they associate it —again\!— with
cause and effect. They will rephrase “If A then B” also as “B because A”
or “B follows from A”. (The use of the words “because” and “follows” is
very revealing\!). Somehow, in the implication “if A then B”, the
antecedent A is associated with the cause and the consequent B with the
effect.

One can defend the thesis that traditional mathematics is
anthropomorphic in the sense that its proofs reflect the causal
hierarchy we discern in our acts, in the same way that traditional logic
—for centuries viewed as the handmaiden of philosophy— is
anthropomorphic in the sense that it tries to formalize and follow our
habits of reasoning.

The advantage of this thesis is that it invites the speculation how
mathematics and logic will evolve when they divest themselves from our
ingrained human reasoning habits, when the role of formalisms will no
longer be to mimic our familiar reasoning patterns but to liberate
ourselves from the latter’s shackles.

And that is a fascinating question to ponder about\!

Austin, 23 September 1985

prof. dr. Edsger W. Dijkstra  
Department of Computer Sciences  
The University of Texas at Austin  
AUSTIN, Texas 78712–1188  
USA

Transcribed by Michael Lugo

Last revised 10 April, 2016 .